ddac49509b
1. IQN gradient sequencing: train_step_gpu replays forward ONLY, caller injects IQN/attention/ensemble gradients into grad_buf, then calls replay_adam_and_readback(). Single Adam sees combined C51+IQN gradient. Previously IQN was a NO-OP (SAXPY happened after both graphs completed — Adam already consumed gradients). 2. Gradient clip 1.0 → 10.0: C51 with 101 atoms × 3 branches produces 300x larger gradients than standard DQN. Clip at 1.0 made effective LR ~7e-12. Result: grad_norm 137K → 490 (280x reduction, network actually learns now). 3. max_abs_reward 3.0 → 1.5: tighter C51 support [-42, +42] instead of [-84, +84]. Q-values at 24 (58% of v_max) instead of 81 (96%). 2x atom resolution. 4. choppy_bonus removed: Flat reward was 0.02 on 60-70% of bars, dominating normalized reward distribution. Now Flat gets exactly 0.0. 5. Reward normalization: Welford EMA was broken (alpha=0.01 over 150K samples → variance converges to zero → divides by 1e-8 → Q-value explosion). Fixed with batch-level mean/std + EMA blending + variance floor 0.01. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Foxhunt
Production HFT trading system in Rust.
Architecture
The workspace contains 32 crates organized as follows:
Core Libraries (16)
| Crate | Purpose |
|---|---|
trading_engine |
Order processing, FIX 4.4, IB TWS, SIMD, RDTSC timing |
risk |
VaR, Kelly, circuit breakers, kill switches, compliance |
risk-data |
Risk data types and shared structures |
trading-data |
Trading data types |
ml |
DQN Rainbow, PPO, TFT, Mamba2, ensemble inference |
ml-data |
ML data types and feature definitions |
data |
Market data ingestion and storage |
backtesting |
Replay engine, strategy tester |
adaptive-strategy |
Ensemble execution, microstructure analysis |
common |
Shared types, resilience, error handling |
storage |
S3 and local model storage |
model_loader |
Model serialization and loading |
market-data |
Market data feed handlers |
database |
PostgreSQL access layer (SQLx) |
config |
Configuration management |
tli |
CLI commands and tooling |
Services (8)
| Service | Purpose |
|---|---|
backtesting_service |
gRPC backtesting service |
broker_gateway_service |
FIX routing, broker connectivity |
trading_service |
Core trading operations |
ml_training_service |
Model training orchestration |
data_acquisition_service |
Market data acquisition |
trading_agent_service |
Autonomous trading agents |
api_gateway |
gRPC API gateway with auth |
web-gateway |
Axum REST + WebSocket gateway |
Frontend
web-dashboard/ -- React 19 + TypeScript + Vite + TradingView charts.
Building
# Check compilation (no PostgreSQL required)
SQLX_OFFLINE=true cargo check --workspace
# Run tests for a specific crate
SQLX_OFFLINE=true cargo test -p <crate> --lib
# Clippy
SQLX_OFFLINE=true cargo clippy --workspace
ML Models
Four production model architectures on Candle v0.9.1 with CUDA:
- DQN Rainbow -- Deep Q-Network with prioritized replay, dueling heads, noisy nets
- PPO -- Proximal Policy Optimization with GAE and LSTM policies
- TFT -- Temporal Fusion Transformer for multi-horizon forecasting
- Mamba2 -- State space model for sequence prediction
Each model has a standalone trainer and a UnifiedTrainable adapter for the hyperopt pipeline.
Infrastructure
- Git: Gitea at
git.fxhnt.ai(Tailscale-only), Scaleway DEV1-S - Observability: OpenTelemetry OTLP (env
OTEL_EXPORTER_OTLP_ENDPOINT) - Database: PostgreSQL with SQLx offline mode for CI
License
Proprietary. All rights reserved.